Synthetic Data-Centric AI. In recent years, person detection and human pose estimation have made great strides, helped by large-scale labeled datasets. Supervised Symbolic Music Style Translation Using Synthetic Data. Using technology from film and gaming, we produce realistic, perfectly labeled training datasets for object detection, segmentation, and 6D pose estimation models. A human-centric framework for context-aware flowable services in cloud computing environments. The Microsoft Mixed Reality & AI Lab - Cambridge sits at the forefront of research, hardware, and software development in the field of social presence in mixed reality. Egocentric vision has a wide range of applications for human-centric activity recognition. machine-learning computer-vision procedural-generation blender synthetic-data synthetic-dataset-generation procedurally-generated. This lack of explainability is unable to satisfy the need for transparency . While some researches utilize 3D models for data generation [41, 26], we could, in turn, employ these models for human . Consistent Data Our datasets are technically homogeneous to allow fast and uncomplicated batching of data. However, as AI tools become more advanced, more computations are done in a "black box" that humans can hardly comprehend. We provide high-performance synthetic data, with a focus on data for human-centric computer vision applications. These methods do not actually capture people in scenes. Comprising a diverse, multidisciplinary team, we approach our work not just as an exciting technological opportunity, but with a responsibility to develop this new medium of 3D communication in an inclusive and ethical way . For each motion sequence per-frame ground truth geometry and ground truth skeleton are given. According to PwC's report on XAI, AI has a $15.7 trillion of opportunity by 2030. Human-Centric Synthetic Data: Research and Applications On-demand webinar Watch industry leaders from Microsoft, The Max Planck Institute for Intelligent Systems, and Datagen share research and best practices for using human-centric synthetic data. We believe it is the real thing; we believe it's time we begin learning and adopting the concept by collaborating with the data-centric AI community . These 3D reconstructions could be good inspiration for novel human representation in action recog-nition task. Salehe Erfanian Ebadi, You-Cyuan Jhang, Alex Zook, Saurav Dhakad, Adam Crespi, Pete Parisi, Steven Borkman, Jonathan Hogins, Sujoy Ganguly. A Data-Centric View of Technical Debt in AI. After nearly a decade of model-centric thinking dominating the fields of machine learning and AI, we're finally seeing a paradigm shift toward a data-centric approach. Experimental studies completed for synthetic data and publicly available data sets are used to realize the algorithm. Synthetic data is in for a banner year, as businesses look to leverage AI for a growing number of increasingly-sophisticated applications, including tackling the world's supply-chain disruptions . State-of-the-art scanning technology is used to create realistic human datasets that enable research scientists to generate reliable synthetic data for ML models. We introduce a synthetic dataset for evaluating non-rigid 3D human reconstruction based on conventional RGB-D cameras. Although there are no clear examples of successful scientific applications of clouds [5], . Download Citation | PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision | In recent years, person detection and human pose estimation have made great strides, helped by . Datagen was founded in 2018 with a mission to transform how teams get their data for computer vision network training. The research revealed that a staggering 96% of computer vision teams reported already using synthetic data in the training and testing of their computer vision models. The dataset consist of seven motion sequences of a single human model. Pull requests. Datagen is powering the AI revolution by providing high-performance synthetic data, with a focus on data for human-centric computer vision applications. Unlike previous work only leveraging synthetic data for model . This repository is demonstrating how Blender can be leveraged to create synthetic machine learning training data for computer vision tasks. The dataset also contains skinning weights of the human model. Several years ago, we introduced the concept of technical debt in machine learning.. We developed the first self-service synthetic data platform that generates visual data which is both . [21] do this by using defined key poses of the body and evaluating human-scene distances and mesh intersections. Deadline for manuscript submissions: closed (30 November 2020) . erate synthetic data for various tasks. In a recent series of talks and related articles, one of the most prominent AI researchers Andrew Ng pointed to the elephant in the room of artificial intelligence: the data. Research on style transfer and domain translation has clearly demonstrated the ability of deep learning-based algorithms to manipulate images in terms of artistic style. A special issue of Entropy (ISSN 1099-4300). Special Issue "Human-Centric AI: The Symbiosis of Human and Artificial Intelligence". This repository provides the codes and data used in our paper "Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art", where we implement and evaluate several state-of-the-art approaches, ranging from handcrafted-based methods to convolutional neural networks. This special issue belongs to the section " Signal and Data Analysis ". However, the use of the egocentric fisheye camera allows wide angle coverage but image distortion is introduced along with strong human body self-occlusion, which can impose significant challenges in data processing and model reconstruction. cifkao/ismir2019-music-style-translation • 4 Jul 2019. The Decade of Synthetic Data is Underway. In the future, when we look back at the development of Artificial Intelligence, the 2020s will be remembered as The Decade of Data. Explainable AI (XAI) in 2022: Guide to enterprise-ready AI. Synthetic training data is the fastest and cheapest way to improve or bootstrap a computer vision model. . Request PDF | Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications | Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0 . Other works like [21, 33] use synthetic 3D scenes and place virtual humans in them to reason about affordances. Updated on Oct 19, 2020. Data-centric AI is pioneering the future development in AI to an extent where limited data sets can realise the operational and business value of integrating AI from concept to production. Our approach could provide rich training data for methods The concept of technical debt originally comes from the world of software engineering, where it has often been found that pushing to develop software very quickly can create long term maintenance costs that must be paid back later, and that if left unaddressed can . It is a common saying in AI that "machine learning is 80% data and 20% models", but in practice, the vast majority of effort from both . ‍ Skip costly hardware setup, data collection, data annotation, and data cleaning. More research studied on various human representations models [7, 39]. DOI: 10.1145/3328519.3329133 Corpus ID: 201809895; Towards an End-to-End Human-Centric Data Cleaning Framework @article{Rezig2019TowardsAE, title={Towards an End-to-End Human-Centric Data Cleaning Framework}, author={El Kindi Rezig and Mourad Ouzzani and Ahmed K. Elmagarmid and Walid G. Aref and Michael Stonebraker}, journal={Proceedings of the Workshop on Human-In-the-Loop Data Analytics . Learn how Microsoft uses synthetic data to make hand tracking possible on HoloLens 2 However, these datasets had no guarantees or analysis of human activities, poses, or context. Using synthetic data from 3-D modeling and rendering of human-activity scenes, LAE-based PI sensing and vision-based remote sensing are emulated and perception systems are formed, showing: 1) enhanced data-efficiency of learning models based on PI sensing; 2) potential for selective deployment of PI sensors in new perception tasks, thanks to . 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